Near-filed SAR Image Restoration with Deep Learning Inverse Technique: A Preliminary Study

Benefiting from a relatively larger aperture's angle, and in combination with a wide transmitting bandwidth, near-field synthetic aperture radar (SAR) provides a high-resolution image of a target's scattering distribution-hot spots. Meanwhile, imaging result suffers inevitable degradation from sidelobes, clutters, and noises, hindering the information retrieval of the target. To restore the image, current methods make simplified assumptions; for example, the point spread function (PSF) is spatially consistent, the target consists of sparse point scatters, etc. Thus, they achieve limited restoration performance in terms of the target's shape, especially for complex targets. To address these issues, a preliminary study is conducted on restoration with the recent promising deep learning inverse technique in this work. We reformulate the degradation model into a spatially variable complex-convolution model, where the near-field SAR's system response is considered. Adhering to it, a model-based deep learning network is designed to restore the image. A simulated degraded image dataset from multiple complex target models is constructed to validate the network. All the images are formulated using the electromagnetic simulation tool. Experiments on the dataset reveal their effectiveness. Compared with current methods, superior performance is achieved regarding the target's shape and energy estimation

Solving 3D Radar Imaging Inverse Problems with a Multi-cognition Task-oriented Framework

This work focuses on 3D Radar imaging inverse problems. Current methods obtain undifferentiated results that suffer task-depended information retrieval loss and thus don't meet the task's specific demands well. For example, biased scattering energy may be acceptable for screen imaging but not for scattering diagnosis. To address this issue, we propose a new task-oriented imaging framework. The imaging principle is task-oriented through an analysis phase to obtain task's demands. The imaging model is multi-cognition regularized to embed and fulfill demands. The imaging method is designed to be general-ized, where couplings between cognitions are decoupled and solved individually with approximation and variable-splitting techniques. Tasks include scattering diagnosis, person screen imaging, and parcel screening imaging are given as examples. Experiments on data from two systems indicate that the pro-posed framework outperforms the current ones in task-depended information retrieval

Mechanism interference critical characterization and autonomous demodulation method of solid filling hydraulic support

Whether the self-demodulation of mechanism interference can be realized in the self-driven execution process of filling operation is the basis for the solid filling hydraulic support to achieve intelligence. Using the theoretical analysis method, taking the ZC5 160/30/50 type solid filling hydraulic support as an example, starting from the geometric and motion constraint relationship of the filling support mechanism, the orthogonal pose control index is established: the horizontal distance and vertical distance of the tamping hinge point top beam, which realizes the pose characterization of the rear top beam of the support under any working condition; The motion characteristics of the bottom-discharge conveyor of the filling support under various working condition factors were analyzed, and the orthogonal pose control index was established: the vertical distance and horizontal distance of the top beam of the bottom-discharge conveyor, which realized the pose characterization of the bottom-discharge conveyor under any working condition; Based on the pose control index of the rear top beam and the bottom-discharge conveyor, the landing position characterization index of the filling material on the coal seam floor during the discharge process is further obtained: the discharge center distance, which realizes the landing position characterization of the filling material under any working condition; the connection relationship and easy interference position of the mechanism action and pose adjustment in each stage of the filling operation process are analyzed, based on the orthogonal pose control index, the interference critical control equation of the discharge and typical collision position under any working condition is established by using the projection method; taking the tamping mechanism rotation angle and tamping cylinder stroke as the characterization of the interference critical curve under typical working conditions, it is proposed to use the three-zone distribution characteristics of “interference zone, easy interference zone, and non-interference zone” to characterize the interference critical degree, and give the demodulation path of each interference state; based on the interference critical control equation, interference three-zone distribution characteristics and the connection relationship of mechanism action and pose adjustment in the filling operation process, an interference state autonomous identification method is proposed: using angle sensor and stroke sensor to obtain the real-time rotation angle and stroke of the tamping mechanism, substituting into the interference critical equation of each easy interference position to obtain the theoretical value and interference critical curve of the tamping mechanism rotation angle or stroke in that position, judging the position of the actual value on the interference critical curve three-zone distribution diagram can realize the autonomous discrimination of interference position and state, and autonomous demodulation can be realized according to the interference three-zone distribution curve diagram; based on the interference position and interference state autonomous identification method, the interference autonomous discrimination and demodulation algorithm is designed. The research results provide new reference indicators for the pose characterization of the filling hydraulic support mechanism, provide basic criteria for the intelligent obstacle avoidance and demodulation of mechanism interference, and provide algorithm basis for the self-driven execution of the filling operation of solid intelligent filling

E2F1 Suppresses Oxidative Metabolism and Endothelial Differentiation of Bone Marrow Progenitor Cells

RATIONALE: The majority of current cardiovascular cell therapy trials use bone marrow progenitor cells (BM PCs) and achieve only modest efficacy; the limited potential of these cells to differentiate into endothelial-lineage cells is one of the major barriers to the success of this promising therapy. We have previously reported that the E2F transcription factor 1 (E2F1) is a repressor of revascularization after ischemic injury. OBJECTIVE: We sought to define the role of E2F1 in the regulation of BM PC function. METHODS AND RESULTS: Ablation of E2F1 (E2F1 deficient) in mouse BM PCs increases oxidative metabolism and reduces lactate production, resulting in enhanced endothelial differentiation. The metabolic switch in E2F1-deficient BM PCs is mediated by a reduction in the expression of pyruvate dehydrogenase kinase 4 and pyruvate dehydrogenase kinase 2; overexpression of pyruvate dehydrogenase kinase 4 reverses the enhancement of oxidative metabolism and endothelial differentiation. Deletion of E2F1 in the BM increases the amount of PC-derived endothelial cells in the ischemic myocardium, enhances vascular growth, reduces infarct size, and improves cardiac function after myocardial infarction. CONCLUSION: Our results suggest a novel mechanism by which E2F1 mediates the metabolic control of BM PC differentiation, and strategies that inhibit E2F1 or enhance oxidative metabolism in BM PCs may improve the effectiveness of cell therapy

Midlatitude Plasma Bubbles Over China and Adjacent Areas During a Magnetic Storm on 8 September 2017

This paper presents observations of postsunset super plasma bubbles over China and adjacent areas during the second main phase of a storm on 8 September 2017. The signatures of the plasma bubbles can be seen or deduced from (1) deep field‐aligned total electron content depletions embedded in regional ionospheric maps derived from dense Global Navigation Satellite System networks, (2) significant equatorial and midlatitudinal plasma bite‐outs in electron density measurements on board Swarm satellites, and (3) enhancements of ionosonde virtual height and scintillation in local evening associated with strong southward interplanetary magnetic field. The bubbles/depletions covered a broad area mainly within 20°–45°N and 80°–110°E with bifurcated structures and persisted for nearly 5 hr (∼13–18 UT). One prominent feature is that the bubbles extended remarkably along the magnetic field lines in the form of depleted flux tubes, reaching up to midlatitude of around 50°N (magnetic latitude: 45.5°N) that maps to an altitude of 6,600 km over the magnetic equator. The maximum upward drift speed of the bubbles over the magnetic equator was about 700 m/s and gradually decreased with altitude and time. The possible triggering mechanism of the plasma bubbles was estimated to be storm time eastward prompt penetration electric field, while the traveling ionospheric disturbance could play a role in facilitating the latitudinal extension of the depletions.Key PointsPostsunset midlatitude plasma bubbles were observed over China and adjacent areas using GNSS TEC, Swarm Ne, and ionosonde dataThe plasma bubbles were triggered by PPEF and TID in equatorial regions and extended along the magnetic field lines to 50°N (45.5 MLAT)Plasma bubbles might reach an altitude of 6,600 km over the magnetic equator with the upper limit of upward drift speed being around 700 m/sPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143723/1/swe20573.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143723/2/swe20573_am.pd

QSYQ Attenuates Oxidative Stress and Apoptosis Induced Heart Remodeling Rats through Different Subtypes of NADPH-Oxidase

We aim to investigate the therapeutic effects of QSYQ, a drug of heart failure (HF) in clinical practice in China, on a rat heart failure (HF) model. 3 groups were divided: HF model group (LAD ligation), QSYQ group (LAD ligation and treated with QSYQ), and sham-operated group. After 4 weeks, rats were sacrificed for cardiac injury measurements. Rats with HF showed obvious histological changes including necrosis and inflammation foci, elevated ventricular remodeling markers levels(matrix metalloproteinases-2, MMP-2), deregulated ejection fraction (EF) value, increased formation of oxidative stress (Malondialdehyde, MDA), and up-regulated levels of apoptotic cells (caspase-3, p53 and tunnel) in myocardial tissue. Treatment of QSYQ improved cardiac remodeling through counter-acting those events. The improvement of QSYQ was accompanied with a restoration of NADPH oxidase 4 (NOX4) and NADPH oxidase 2 (NOX2) pathways in different patterns. Administration of QSYQ could attenuate LAD-induced HF, and AngII-NOX2-ROS-MMPs pathway seemed to be the critical potential targets for QSYQ to reduce the remodeling. Moreover, NOX4 was another key targets to inhibit the p53 and Caspase3, thus to reduce the hypertrophy and apoptosis, and eventually provide a synergetic cardiac protective effect

Intelligent reinforcement training optimisation of dispatch strategy for provincial power grids with multi‐agent systems: Considering operational risks and backup availability

Abstract In order to optimise resource allocation within the province, a two‐stage scheduling model for provincial‐level power grids, encompassing day‐ahead and intra‐day stages is proposed. Firstly, a Conditional Generative Adversarial Network is employed to generate scenarios for load and new energy output. Based on the generated scenario set, the model takes into account the uncertainty and permissible error intervals of new energy and load, utilising conditional value at risk to measure the system scheduling risk. In the day‐ahead stage, an optimisation model is proposed, considering intra‐provincial power purchase demands, with the goal of minimising system operating costs, including risk costs. It optimises day‐ahead scheduling and contingency plans to ensure economic efficiency and robustness of the system based on extreme scenarios. During the training phase, the dataset is enhanced using Conditional Generative Adversarial Network and updated daily, improving the training effectiveness of the multi‐agent proximal policy optimisation intra‐day scheduling model. In the intra‐day stage, the intra‐day scheduling model utilises ultra‐short‐term forecasting data as input to generate contingency plans for dispatching reserve units. Experiments conducted on the IEEE 39‐node system validate the feasibility and effectiveness of the proposed approach

Adamantane-Modified Graphene Oxide for Cyanate Ester Resin Composites with Improved Properties

The conjugation of graphene and polymers has attracted great attention for the fabrication of functional hybrid nanomaterials. Here, we demonstrate the modification of graphene oxide (GO) with adamantane (AMT) through the diimide-activated amidation reaction. The modification of GO with AMT improves the dispersion and decreases the interfacial polarization of GO, causing a lower dielectric constant for the fabricated GO/AMT hybrid materials. The structures of GO/AMT were studied by Fourier transform infrared spectroscopy and Raman spectroscopy. Furthermore, the mechanical properties, thermal stability, and dielectric constant of GO/AMT composites were measured at a low cured temperature using various techniques, such as differential scanning calorimetry, thermogravimetric analysis, and dynamic mechanical thermal analysis. It was found that the synthesized GO/AMT materials with different contents were blended into cyanate ester (CE) resins, resulting in a lower cure temperature, smaller dielectric constant, higher thermal stability, and stronger water resistance. It is expected that this novel GO/AMT-CE material will have potential applications for replacing traditional thermosetting resins

A Group-Wise Feature Enhancement-and-Fusion Network with Dual-Polarization Feature Enrichment for SAR Ship Detection

Ship detection in synthetic aperture radar (SAR) images is a significant and challenging task. However, most existing deep learning-based SAR ship detection approaches are confined to single-polarization SAR images and fail to leverage dual-polarization characteristics, which increases the difficulty of further improving the detection performance. One problem that requires a solution is how to make full use of the dual-polarization characteristics and how to excavate polarization features using the ship detection network. To tackle the problem, we propose a group-wise feature enhancement-and-fusion network with dual-polarization feature enrichment (GWFEF-Net) for better dual-polarization SAR ship detection. GWFEF-Net offers four contributions: (1) dual-polarization feature enrichment (DFE) for enriching the feature library and suppressing clutter interferences to facilitate feature extraction; (2) group-wise feature enhancement (GFE) for enhancing each polarization semantic feature to highlight each polarization feature region; (3) group-wise feature fusion (GFF) for fusing multi-scale polarization features to realize polarization features’ group-wise information interaction; (4) hybrid pooling channel attention (HPCA) for channel modeling to balance each polarization feature’s contribution. We conduct sufficient ablation studies to verify the effectiveness of each contribution. Extensive experiments on the Sentinel-1 dual-polarization SAR ship dataset demonstrate the superior performance of GWFEF-Net, with 94.18% in average precision (AP), compared with the other ten competitive methods. Specifically, GWFEF-Net can yield a 2.51% AP improvement compared with the second-best method

Target-Oriented High-Resolution and Wide-Swath Imaging with an Adaptive Receiving–Processing–Decision Feedback Framework

High-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) is a promising technique for applications such as maritime surveillance. In the maritime environment, normally only a few targets such as ships are interested. However, before detecting them, considerable receiving resources and computation time are required to receive the echoes of the whole scene and process them to obtain imaging results. This is a heavy burden for online monitoring on platforms with limited resources. To address these issues, different from the concept of whole-scene-oriented imaging, we propose a target-oriented imaging concept, which is implemented by an adaptive receiving–processing–decision feedback framework with feedback connection. (1) To reduce receiving resource consumption, we propose a two-dimensional adaptive receiving module. It receives sub-band echoes of targets only through dechirping and subaperture decomposition in the range and azimuth directions, respectively. (2) To reduce computation time, we propose a target-oriented processing module. It processes subarea echoes of targets only through parallelly conducting inverse fast Fourier transform (IFFT) and back projection (BP) in the range and azimuth directions, respectively. (3) To allocate resources reasonably, we propose a decision module. It decides the necessary receiving window, bandwidth, and image resolution through constant false alarm rate (CFAR) detection. (4) To allocate resources adaptively, we connect three modules with a closed loop to enable feedback. This enables progressive target imaging and detection from rough to fine. Experimental results verify the feasibility of the proposed framework. Compared with the current one, for a typical scenario, at least 30% of the system’s resources and 99% of computation time are saved